SOTAVerified

Point Cloud Segmentation

3D point cloud segmentation is the process of classifying point clouds into multiple homogeneous regions, the points in the same region will have the same properties. The segmentation is challenging because of high redundancy, uneven sampling density, and lack explicit structure of point cloud data. This problem has many applications in robotics such as intelligent vehicles, autonomous mapping and navigation.

Source: 3D point cloud segmentation: A survey

Papers

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Showing 201–225 of 272 papers

TitleStatusHype
Uncertainty Estimation in Deep Neural Networks for Point Cloud Segmentation in Factory Planning—0
Self-Supervised Learning of Lidar Segmentation for Autonomous Indoor NavigationCode1
Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene CompletionCode1
GndNet: Fast Ground Plane Estimation and Point Cloud Segmentation for Autonomous VehiclesCode1
PBP-Net: Point Projection and Back-Projection Network for 3D Point Cloud Segmentation—0
3D photogrammetry point cloud segmentation using a model ensembling framework—0
Stereo Frustums: A Siamese Pipeline for 3D Object Detection—0
UAV LiDAR Point Cloud Segmentation of A Stack Interchange with Deep Neural Networks—0
Unsupervised Point Cloud Pre-Training via Occlusion CompletionCode1
Semantic Segmentation of Surface from Lidar Point Cloud—0
ePointDA: An End-to-End Simulation-to-Real Domain Adaptation Framework for LiDAR Point Cloud Segmentation—0
Generating synthetic photogrammetric data for training deep learning based 3D point cloud segmentation models—0
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor SceneCode1
Rethinking 3D LiDAR Point Cloud SegmentationCode0
Semantic Context Encoding for Accurate 3D Point Cloud Segmentation—0
Deep FusionNet for Point Cloud Semantic Segmentation—0
Cascaded Non-local Neural Network for Point Cloud Semantic Segmentation—0
KPRNet: Improving projection-based LiDAR semantic segmentationCode1
Cloud Transformers: A Universal Approach To Point Cloud Processing Tasks—0
SASO: Joint 3D Semantic-Instance Segmentation via Multi-scale Semantic Association and Salient Point Clustering Optimization—0
Few-Shot Learning of Part-Specific Probability Space for 3D Shape Segmentation—0
Weakly Supervised Semantic Point Cloud Segmentation: Towards 10x Fewer LabelsCode1
SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel—0
Dense-Resolution Network for Point Cloud Classification and SegmentationCode1
Fast Geometric Surface based Segmentation of Point Cloud from Lidar Data—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OcCo-PCNmean Corruption Error (mCE)1.17—Unverified
2OcCo-PointNetmean Corruption Error (mCE)1.13—Unverified
3PointNet++mean Corruption Error (mCE)1.11—Unverified
4PointTransformersmean Corruption Error (mCE)1.05—Unverified
5PointMLPmean Corruption Error (mCE)0.98—Unverified
6PointMAEmean Corruption Error (mCE)0.93—Unverified
7GDANetmean Corruption Error (mCE)0.92—Unverified
8GDANetmean Corruption Error (mCE)0.89—Unverified